Ph.D. Theses

A Model for Resource-Aware Load-Balancing on
Heterogeneous and Non-Dedicated Clusters

As clusters become increasingly popular alternatives to custom-built
parallel computers, they expose a growing heterogeneity in processing
and communication capabilities. Performing an effective load balancing
on such environments can be best achieved when the heterogeneity factor
is quantified and appropriately fed into the load balancing routines.

In this work, we discuss an approach based on constructing a
tree model that encapsulates the topology and the capabilities of the
cluster. The different components of the execution environment are
dynamically monitored and their processing and communication capabilities
are collected and aggregated in a simple form easily usable when load
balancing is invoked.

We used the model, called DRUM, to guide load balancing in the adaptive
solution of three numerical problems on various cluster configurations.
The results showed a clear benefit from using DRUM, in clusters with
computational or network heterogeneity as well as in non-dedicated clusters.
The results also showed that DRUM generates a very low overhead.